The paper improves generalization bounds for classifier chains with interdependent labels.
problem Improving generalization for classifier chains with multiple interdependent labels.
method Using large deviation inequalities for weakly dependent sequences, the paper derives a new generalization error bound.
result The derived bound explicitly shows dependencies between class labels and provides insights into the chain's order.
Graph Posterior Network improves uncertainty estimation for node classification in interdependent graphs.
problem Uncertainty quantification for non-independent node-level predictions in graphs.
method Derives axioms for expected predictive uncertainty, proposes Graph Posterior Network (GPN) which performs Bayesian posterior updates.
result GPN outperforms existing approaches for uncertainty estimation in semi-supervised node classification.
Paper introduces methods for more reliable probabilistic predictions with confidence intervals.
problem Inaccurate labeling of datasets due to unreliable probabilistic predictions from weak labeling functions.
method Proposes a methodology to provide confidence intervals for label probabilities using uncertainty sets of distributions.
result Improves reliability of probabilistic predictions and provides confidence intervals for label probabilities.
Study the Mexican stock market's interdependency structure from 2000-2019.
problem Characterize the interdependency structure of the Mexican Stock Exchange.
method Estimate correlation/concentration matrices from different models and compute network theory metrics.
result Visualizations provide a comprehensive overview of the stock market's interdependency structure.
RPN 2 improves function learning by modeling data interdependence.
problem Invalid assumption of input data independence leads to performance degradation.
method Integrates data and structural interdependence functions into RPN 2's architecture.
result Significantly improves learning performance and expands unifying potential.
Model shows how financial contagion spreads through complex interdependencies.
problem Understanding how banks fail in an interconnected financial system.
method Unified model combining direct and indirect dependencies; three reconstruction methods.
result Hierarchical cascades reveal dominant banks in failures.
New measure shows how LSTM models compose hierarchical representations.
problem Understanding how LSTM models capture compositional structure in language.
method Novel measure of interdependence between word meanings in LSTM internal gates.
result High interdependence can hurt generalization and reveals hierarchical structure learning.
This paper develops a federated approach to learn Granger causality in interdependent industrial clients.
problem Detecting and quantifying interdependencies in large, complex industrial data.
method Linear state space system framework, federated learning, differential privacy.
result Federated Granger causality learning addresses bandwidth and computational limitations.
This paper is concerned with structured machine learning, in a supervised machine learning context. It discusses how to make joint structured learning on interdependent objects of different nature, as well as how to enforce logical con-straints when predicting labels. We explain how this need arose in a Document Unders…
Analyzes global economic sectors' interdependence using Google matrix analysis.
problem Understanding interdependencies and interactions among world economies and sectors.
method Reduced Google matrix algorithm applied to OECD-WTO network data.
result Shows sensitivity of sectors to petroleum activity and interdependencies among countries.
Paper tackles RCA in complex networks with unknown interdependencies.
problem Difficult RCA in networked systems due to unknown interdependencies.
method Federated learning for feature-partitioned, nonlinear data without modifying client models.
result Established theoretical convergence guarantees and validated on real-world data.
CAUSE learns Granger causality from event sequences, outperforming existing methods.
problem Learning Granger causality from complex, interdependent event sequences.
method CAUSE uses a neural point process to capture interdependency and an attribution method to extract Granger causality.
result CAUSE outperforms state-of-the-art methods in inferring inter-type Granger causality.
Paper compares largest claim amounts from two interdependent portfolios.
problem Comparing claim amounts from two sets of interdependent portfolios.
method Stochastic comparisons using dependent non-negative random variables and Bernoulli variables.
result Stochastic order results for largest claim amounts.
To identify emerging interdependencies between traded stocks we investigate the behavior of the stocks of FTSE 100 companies in the period 2000-2015, by looking at daily stock values. Exploiting the power of information theoretical measures to extract direct influences between multiple time series, we compute the infor…
We generalize the scale-free network model of Barabàsi and Albert [Science 286, 509 (1999)] by proposing a class of stochastic models for scale-free interdependent networks in which interdependent nodes are not randomly connected but rather are connected via preferential attachment (PA). Each network grows through the …
In the current era of worldwide stock market interdependencies, the global financial village has become increasingly vulnerable to systemic collapse. The recent global financial crisis has highlighted the necessity of understanding and quantifying interdependencies among the world's economies, developing new effective …
Extends Bayesian theory to handle complex interdependencies in multidimensional event spaces.
problem Complex interdependencies between events and hypotheses sets in real-world systems.
method Developed a mathematical formalism for modeling complex relationships through rigorous derivation and validated using analytical proofs, simulations, and case studies.
result MDSE theory improves prediction accuracy by 15-20% compared to standard Bayesian methods in high interdimensionality datasets.
ORDisCo learns from unlabeled data to improve semi-supervised continual learning.
problem Lack of effective use of unlabeled data in semi-supervised continual learning.
method Deep Online Replay with Discriminator Consistency (ORDisCo) that continually passes the learned data distribution to a classifier and selectively stabilizes discriminator parameters.
result Significant performance improvement on various semi-supervised learning benchmark datasets.
Study combines speaker verification and voice trigger detection in a single network.
problem Separate training for speaker verification and voice trigger detection.
method Multi-task learning with a single network trained on both tasks.
result Single network achieves comparable accuracy to independent models for each task.
A new random forest algorithm uncovers feature interdependencies better than traditional methods.
problem Tackles the sub-optimality of greedy decision tree implementations in random forests.
method Presented a 'stepwise lookahead' variation of random forests that considers multiple split nodes simultaneously.
result Significantly outperforms greedy random forests in uncovering feature interdependencies, especially in high-noise environments.
Federated learning interprets temporal dynamics across clients with graph attention.
problem Interpreting temporal patterns across decentralized, heterogeneous systems with nonlinear dynamics.
method Graph Attention Network for learning state transition models over latent states communicated between clients.
result First interpretable characterization of cross-client temporal interdependencies in decentralized nonlinear systems.
Study maps interdependence of SDGs, finds complex, dynamic linkages.
problem Identify which SDGs promote progress and how quickly.
method Used a balanced panel of 114 countries from 2000 to 2024, applying two estimators to recover directed interaction network and measure dynamic linkages.
result 84 goal linkages survive false-discovery control, showing both synergies and trade-offs, with no single goal acting as a universal accelerator.
Paper compares smallest claim amounts from two interdependent portfolios.
problem Comparing smallest claim amounts from two sets of interdependent portfolios.
method Dependent non-negative random variables with survival copula, Bernoulli random variables, likelihood ratio order.
result Bounds for survival function of the smallest claim amount in a portfolio.
Empirical study on hyperparameters affecting RL generalization.
problem Understanding how hyperparameters affect RL generalization across different domains.
method Empirical analysis of hyperparameters including architecture, regularization, and RL-dependent variables.
result Complex and interdependent relationships between hyperparameters and generalization performance.
Automated model tracks mouse behavior in home cages.
problem Confounding analysis in specialized arenas.
method Group Behaviour Model (GBM) using permutation matrix.
result Model captures individual and group behavior.
New approach for causal inference with interdependent, time-varying latent confounders.
problem Estimating causal effects with interdependent, time-varying latent confounders.
method Variational estimation with a representer theorem and random input space.
result Demonstrates effectiveness on various temporal datasets.
This study maps systemic risks in TradFi and DeFi, highlighting their interdependence.
problem Systemic risks in traditional and decentralized finance.
method Conceptual model and comparative analysis of TradFi and DeFi.
result Systemic risks in DeFi can affect TradFi and vice versa, creating a crosstagion effect.
Through a long-period analysis of the inter-temporal relations between the French markets for credit default swaps (CDS), shares and bonds between 2001 and 2008, this article shows how a financial innovation like CDS could heighten financial instability. After describing the operating principles of credit derivatives i…
We present a new approach to estimating the interdependence of industries in an economy by applying data science solutions. By exploiting interfirm buyer--seller network data, we show that the problem of estimating the interdependence of industries is similar to the problem of uncovering the latent block structure in n…
Simultaneously estimates travel times and route choice model parameters.
problem Interdependent estimation of arc travel times and route choice model parameters.
method Maximum likelihood estimation for any differentiable route choice model.
result Strong performance in real-world data, even compared to arc travel time estimation methods.
The large-scale organization of the world economies is exhibiting increasingly levels of local heterogeneity and global interdependency. Understanding the relation between local and global features calls for analytical tools able to uncover the global emerging organization of the international trade network. Here we an…
A new convolutional spectral kernel network learns hierarchical and local features.
problem Lack of deep learning in non-stationary spectral kernels.
method Introduces convolutional filters and deep architectures into non-stationary spectral kernels, derives generalization error bounds, and introduces regularizers.
result Validated the effectiveness of the convolutional spectral kernel network on real-world datasets.
Cross-border equity and long-term debt securities portfolio investment networks are analysed from 2002 to 2012, covering the 2008 global financial crisis. They serve as network-proxies for measuring the robustness of the global financial system and the interdependence of financial markets, respectively. Two early-warni…
The paper analyzes the crash of stock and commodity markets during COVID-19 using Topological Data Analysis.
problem Identifying and understanding the dynamics and interdependence of stock and commodity markets during the COVID-19 crash.
method Topological Data Analysis (TDA) and Wasserstein Distance (WD) to identify crashes and compare market dynamics.
result Significant topological differences and interdependence between stock and commodity markets during the crash period.
New approach protects privacy of deleted records in machine learning.
problem Privacy of deleted records in machine learning models.
method Sound deletion guarantee and noisy gradient descent algorithm.
result Privacy of existing records is necessary for deleted records' privacy.
This paper presents a novel decentralized high-dimensional Bayesian optimization (DEC-HBO) algorithm that, in contrast to existing HBO algorithms, can exploit the interdependent effects of various input components on the output of the unknown objective function f for boosting the BO performance and still preserve scala…
Following Goussarov's paper `Interdependent Modifications of Links and Invariants of Finite Degree' [Topology 37 (1998) 595--602] we describe an alternative finite type theory of knots. While (as shown by Goussarov) the alternative theory turns out to be equivalent to the standard one, it nevertheless has its own share…
Empirical study finds IT project costs follow a power-law distribution, exposing risk underestimation.
problem IT project cost overruns are underestimated due to normal distribution assumptions.
method Analyzed 5,392 IT projects to examine cost overruns following a power-law distribution.
result IT project cost overruns follow a power-law distribution with a fat tail of extreme overruns.
Develops auction theory for real-life applications with positive valuations.
problem Real-life auction settings with positive valuations and interdependent bidders.
method Approximations using log-normal distribution, positive symmetric discrete distribution, and interdependent valuations.
result New auction theory results applicable to finance and procurement.
The paper solves portfolio optimization problems with risk constraints.
problem Maximizing utility while ensuring a certain wealth threshold with risk constraints.
method Derives Nash equilibria for two agents and characterizes them for more than two agents.
result Characterizes Nash equilibria for different cases of competition probabilities.
Scene understanding remains a significant challenge in the computer vision community. The visual psychophysics literature has demonstrated the importance of interdependence among parts of the scene. Yet, the majority of methods in computer vision remain local. Pictorial structures have arisen as a fundamental parts-bas…
CDA framework infers channel influence from aggregated data without user identifiers.
problem Lack of user-level path data due to privacy regulations and platform restrictions.
method CDA integrates PCMCI for causal discovery and Structural Causal Model for effect estimation.
result CDA achieves strong accuracy in estimating channel influence, even under structural uncertainty.
This paper uses Factored Latent Analysis (FLA) to learn a factorized, segmental representation for observations of tracked objects over time. Factored Latent Analysis is latent class analysis in which the observation space is subdivided and each aspect of the original space is represented by a separate latent class mod…
Federated framework learns causal states to predict counterfactuals without centralizing data.
problem Decentralized counterfactual reasoning in coupled industrial systems with private data.
method Federated causal representation learning in state-space systems.
result Proves convergence to centralized oracle and provides privacy guarantees.
Improved AI patent classifier measures U.S. and China's AI patenting.
problem Measuring AI patents with high precision and generalization.
method Fine-tuning PatentSBERTa on manually labeled data from USPTO's AI Patent Dataset.
result Rapid growth in AI patenting in both countries, but different organizational patterns.
We discuss social network analysis from the perspective of economics. We organize the presentaion around the theme of externalities: the effects that one's behavior has on others' well-being. Externalities underlie the interdependencies that make networks interesting. We discuss network formation, as well as interactio…
Paper introduces a new framework for spatio-temporal structured sparse regression.
problem Reconstructing spatio-temporal evolving patterns with high accuracy.
method Hierarchical Gaussian process with expectation propagation for online and offline Bayesian inference.
result 15% improvement in F-measure compared to existing methods.
Mixed-integer programming solves systemic risk measures for interdependent financial systems.
problem Computing systemic risk measures for interdependent financial systems with joint risk considerations.
method Proposes a mixed-integer programming problem to compute clearing vectors in a Rogers-Veraart network model with unrestricted sign operating cash flows.
result The proposed mixed-integer programming problem can compute systemic risk measures for interdependent financial systems.